The study aimed at examining native English teachers’ and Korean EFL teachers’ understanding and teaching experience of epistemic modals. For the study, a total of 51 teachers participated in the online survey, and 11 of them had a follow-up interview. The results revealed that Korean teachers’ understanding was surprisingly low, indicating 10.94% of accuracy rate on average while native English teachers showed 94.74% of accuracy rate. The two groups’ mean score was statistically significant (p<.05). Concerning both groups of the teachers’experience of epistemic modals, while 78% of them had teaching experiences, both groups of the teachers merely focused on forms and meanings, not semantic system, which concerned a hierarchy for logical meanings. Finally, significance of understanding and using functions of epistemic modals was discussed.

According to the taxonomy of English modals by Hofmann (1966) and Palmer (1990), the present study aimed at examining Korean EFL teachers’understanding and teaching experience of semantic concepts of epistemic modals. For the data, a total of 42 teachers enrolled in the English education program at 2 graduate schools in South Korea participated in an online survey. Of the participants, 18 participated in a face-to-face interview. The findings revealed that the teachers’ understanding of English epistemic modals was very low, indicating 11.92% of accuracy rate on average. In particular, their understanding of expressing speakers’ positive inference showed the lowest accuracy rate (0%). Regarding the teachers’ experience of teaching epistemic modals, the survey indicated that more than 60% of the teachers had teaching experiences. However, the teachers merely focused on forms and meanings, not functions. Importance of being aware of the semantic systems of epistemic modals, which were to express a hierarchy for logical meanings, was discussed.

Yong-hun Lee, JeeHee Yu, and Tae-Jin Yoon. 2017. Predicting the Occurrence of the English Modals Can and May Using Deep Neural Networks. Studies in Modern Grammar 96, 167-189. This paper tries to provide a computational modeling of language processing using deep neural networks. For this purpose, the corpus data in the ICE-USA was used. After all the sentences with can and may were encoded with eighteen linguistic factors, the annotated data were fed into the deep neural networks (DNN). The DNN was constructed with three layers, and each layer contained seventeen nodes. After the DNN was constructed, the learning process was performed with a training set. Then, the performance was measured with a test set. The processes were repeated one hundred times, and it was observed that the DNN had the classification accuracy of 91.5%. The results are promising in that reliable methods can be used in automatically classifying the frequently used modal auxiliary on the basis of the deep learning system.